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Speaker 1: Hello, welcome to Fast Politics. I'm Molly Jongfast. We have

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Speaker 1: a great show for you today. Later in the show,

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Speaker 1: we'll talk to MSNOW's Ali Velshi about Trump's disastrous economy.

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Speaker 1: But first, we have Emmy-winning producer for Veep and Succession,

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Speaker 1: the great Frank Rich on Trump's Willing Collaborators. Welcome, welcome,

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Speaker 1: Frank Rich.

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Speaker 2: Hi, nice to be on with you, Molly. How are you?

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Speaker 1: I'm good. So you wrote this amazing piece and you

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Speaker 1: are one of the great political writers of all time.

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Speaker 1: So Susie Wiles is feels like a character right out

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Speaker 1: of one of your shows.

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Speaker 2: I'm sorry.

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Speaker 1: She really does. I mean, when and she's now in peril.

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Speaker 1: uh can we talk.

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Speaker 2: About this well i can find sure i i mean

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Speaker 2: if you're talking about veep i think susie susan wells

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Speaker 2: is just completely unfunny so i don't know if she's

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Speaker 2: you know she's no kimberly gilfoyle you know what i

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Speaker 2: mean she's no christy gnome uh it's a very high

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Speaker 2: bar here there's uh she's she's she doesn't rise remotely

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Speaker 2: to the level of our new press secretary who can't

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Speaker 2: even figure out what her career was or where she's

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Speaker 2: been at any given time. I do think the Machiavellian

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Speaker 2: stuff that seems to be going on, keep in mind,

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Speaker 2: it's all rumor. I mean, I read the Washington Post

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Speaker 2: moved a big story overnight about internal dissent and that

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Speaker 2: she's likely to go, but they actually have no one

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Speaker 2: saying she's actually going. And maybe Trump, but it wouldn't

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Speaker 2: surprise me if she's fired. I think she's far from

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Speaker 2: the most nefarious person in that White House, not that

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Speaker 2: she's good, but there'll be a lot of people taking

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Speaker 2: the blame if the election results go the way everyone

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Speaker 2: thinks they're going to go. And it certainly hasn't helped

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Speaker 2: that two of her flunkies had been helped during the

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Speaker 2: height of the campaign or these political aides who worked

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Speaker 2: for Trump and supposedly for the Republican reelection effort have been,

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Speaker 2: you know, helping a pro-Putin government for cash abroad. And

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Speaker 2: they're her people. So it's It's it's on her.

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Speaker 1: Yeah, that that's true.

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Speaker 3: It is.

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Speaker 1: That is that article, which I read this morning, too,

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Speaker 1: where it's like and there was another article that was

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Speaker 1: a little bit like that earlier in the week. There

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Speaker 1: was like who Donald Trump is going to blame for

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Speaker 1: the results of the midterms. Like, we're so, that feels

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Speaker 1: like Veep.

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Speaker 3: Like, you know you're going to lose.

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Speaker 1: You're setting up the people. James Blair is, like, number

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Speaker 1: one person that Donald Trump is going to blame for

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Speaker 1: the midterms.

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Speaker 2: No, number one is Joe Biden. Right, right. And number

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Speaker 2: two may be Obama. Right. And then Blair and, you know,

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Speaker 2: these other clowns. But it's going to be interesting to

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Speaker 2: see what happens with him. He's obviously not being told

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Speaker 2: what's going on. I mean, he's out of touch with

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Speaker 2: reality more than usual. And while some of it may

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Speaker 2: be that his normal state, a lot of it is,

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Speaker 2: it seems like he's really in a bubble, even by

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Speaker 2: his standards, and doesn't quite really know what the polls

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Speaker 2: are saying. Some of it's just, of course, crazy or

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Speaker 2: just lying, like when he's at a one-third empty arena

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Speaker 2: and claiming people are waiting outside to fill the empty seats.

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Speaker 2: That's probably just lying and not actually dementia. Maybe it's dementia.

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Speaker 2: I don't know. But it's going to be interesting to

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Speaker 2: see how he lashes out. He's already lashing out. And

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Speaker 2: maybe he'll bring back the firing squad, the televised firing squad.

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Speaker 3: That's right.

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Speaker 1: Yes. Yesterday, Donald Trump said no televised firing squad whatsoever.

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Speaker 1: which in 19 days before the midterms or 20 days

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Speaker 1: before the midterms, saying no to the firing squad feels like,

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Speaker 1: you know, just a family-friendly move.

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Speaker 2: Yes, but also it shows, someone told them there was

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Speaker 2: an incredible backlash, not just among politicians, but I think

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Speaker 2: there was a poll, I think it was a legitimate poll,

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Speaker 2: that showed 80% of the country disapproved of live streaming,

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Speaker 2: something like that. Yeah. And so it's just like, On

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Speaker 2: the other hand, he hasn't reversed buying diesel fuel from

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Speaker 2: the Russians. I mean, you never know what gets in

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Speaker 2: the way. And that's probably doing him more damage anyway.

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Speaker 1: Donald Trump did a talk in the Rose Garden yesterday

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Speaker 1: for firefighters. And in it, what's amazing about listening to

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Speaker 1: Donald Trump's speeches is he gets these ideas that are

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Speaker 1: almost poetic. And they're very.

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Speaker 3: The weave.

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Speaker 2: Yeah, go ahead.

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Speaker 1: Right, the weave. And so he said, you know, if somebody...

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Speaker 1: And they're clearly like something that has occupied his brain.

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Speaker 1: So he said, if somebody tries to kill a firefighter,

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Speaker 1: on a ladder, they should get the death penalty.

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Speaker 2: Well, how often has anyone done that? I don't even

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Speaker 2: know what that means. I've heard of firemen tragically falling

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Speaker 2: from a ladder or being pushed off a ladder by flames,

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Speaker 2: but someone trying, I mean, I don't know. At a

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Speaker 2: certain point, it defies exegesis of any kind, what the

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Speaker 2: hell he's thinking or doing.

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Speaker 1: But it is, it speaks to the disinhibited nature of Trump, right?

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Speaker 1: Where he can't, at this point, and you and I

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Speaker 1: have been talking about this guy for a decade, and

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Speaker 1: I just wonder, like, it's very hard to pull back

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Speaker 1: from that disinhibited state.

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Speaker 2: I think his personality seems to be disintegrating in some ways.

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Speaker 2: It's all recognizable traits of his that behavior we're seeing

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Speaker 2: right now. But it's so scattershot and self-destructive in the

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Speaker 2: political sense. And I'm talking about the biological and psychological sense.

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Speaker 2: But for instance, obviously his staff said, please stop talking

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Speaker 2: about your ballroom. Stop talking about the Kennedy Center. Stop

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Speaker 2: talking about the reflecting pool and your arch. People want

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Speaker 2: to hear you talk about affordability, which of course, when

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Speaker 2: he speaks about it, only says this is to say

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Speaker 2: it's a hoax. And so if you're showing off a gilded,

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Speaker 2: gold-saturated ballroom to voters right now. I mean, obviously, they're

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Speaker 2: begging everyone political around him, if he's listening, they would say,

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Speaker 2: don't do it. But he can't stop himself. And he's

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Speaker 2: used to having no breaks. And now he's had no

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Speaker 2: breaks officially, thanks to the Supreme Court and a party

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Speaker 2: that's completely sold out and the Congress has been his slave.

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Speaker 2: And so there's nothing To stop him from saying or

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Speaker 2: doing anything, even, you know, I spoke to an old

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Speaker 2: friend of Tony Schwartz who wrote The Art of the

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Speaker 2: Deal for Trump and been repenting ever since and is

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Speaker 2: a very shrewd analyst of Trump's psyche. And I said,

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Speaker 2: it's almost like he's trying to lose some of the

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Speaker 2: stuff he's doing. It's so crazy, you know, attacking a

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Speaker 2: Republican candidate's looks and Texas and wardrobe. I mean, crazy stuff.

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Speaker 2: And I raised it. question is sort of an A-promp

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Speaker 2: operating model, a deluge thing where Trump wants them all

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Speaker 2: to lose so he can say, only I can fix it.

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Speaker 2: If I had been on the ballot, he's definitely going

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Speaker 2: to say, if only I had been on the ballot,

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Speaker 2: it wouldn't have happened. But he feels it's reached a

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Speaker 2: point of where it's a kind of psychological disorder possibly

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Speaker 2: combined with dementia that isn't even that much in touch

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Speaker 2: with reality or cause and effect. It's so scattered and weird.

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Speaker 1: Yeah, I think that's totally true. And Tony Schwartz has

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Speaker 1: really tried to make this poor guy, you know, he

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Speaker 1: didn't know at the time when he.

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Speaker 2: Was- Well, it was 1987. Right. No, it was 1987.

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Speaker 2: He was writing the book to make some money as

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Speaker 2: he was, you know, a young journalist. And Trump was

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Speaker 2: just a real estate entrepreneur then. And of course, Tony

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Speaker 2: wrote the book. It's not clear that Trump ever read it.

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Speaker 2: But anyway, that's another story.

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Speaker 3: It's true.

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Speaker 1: But it was a very successful book, which was, you know,

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Speaker 1: when you're too good, it goes better. Yeah, yeah.

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Speaker 2: Yeah.

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Speaker 1: And one of the things you mentioned is this idea

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Speaker 1: that Donald, what Donald Trump on Ken Paxton, where he says,

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Speaker 1: you know, the guy, this Ken Paxton, the guy that

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Speaker 1: Donald Trump made the nominee for Texas, right?

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Speaker 2: Yes, over a safer nominee over, yes, John Cornyn, the incumbent,

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Speaker 2: threw him aside for this, you know, criminal, basically. And

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Speaker 2: go ahead. I didn't mean to interrupt your sentence.

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Speaker 1: Yeah.

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Speaker 3: Right.

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Speaker 1: A man who had been impeached but not removed, just

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Speaker 1: like Trump. And then Trump says, you know, he's not

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Speaker 1: a good dresser. He speaks terribly. He's got the... You know,

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Speaker 1: we just can't say nice things about him.

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Speaker 3: Yeah.

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Speaker 2: And so... And he says it while the guy is

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Speaker 2: in the audience at a rally. And it's just... It's

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Speaker 2: so strange. And then you have, of course, the candidate, Paxton...

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Speaker 2: being caught on various recordings and various sessions criticizing Trump

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Speaker 2: and saying the Trump convention backfired on him. And apparently,

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Speaker 2: I was just reading a smart piece about polling somewhere today.

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Speaker 2: The polling, not just from Paxton, but the Republicans generically

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Speaker 2: collapsed after that midterm convention. And it gets back to

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Speaker 2: my point. He's talking about things people don't want to

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Speaker 2: hear about, that voters don't want to hear about, and

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Speaker 2: some of the things they're actually interested in. And when

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Speaker 2: he talks about the things they're interested in, he either

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Speaker 2: says something like, you know, you're an idiot if you

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Speaker 2: don't like, you know, data, data, you know, identity centers

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Speaker 2: in your backyard, that you're backwards and you're stupid. And

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Speaker 2: or no, there's no prices are all down. They're coming down.

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Speaker 2: A shred of diesel oil from Russia is going to

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Speaker 2: somehow bring back the price of diesel. I mean, it's

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Speaker 2: loony tunes, really. It's kind of entertaining in a horrible way,

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Speaker 2: but anyway.

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Speaker 1: It's funny. I want to talk about the collaborators, but

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Speaker 1: I just want to agree with you. In this speech

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Speaker 1: for the firefighters, Donald Trump is talking about how glad

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Speaker 1: he is they moved the helipad. because his feet don't

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Speaker 1: get wet anymore, walking on the grass.

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Speaker 4: He said this?

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Speaker 3: Yes.

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Speaker 1: And I was like, wow. So why don't you do

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Speaker 1: a cut of all the times he talks about- Wow,

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Speaker 1: that's what I had.

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Speaker 3: Yeah, I heard it. I was like, wow.

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Speaker 1: So let's talk about this piece, because I just, you know,

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Speaker 1: the collaborators, the cronies of Trump's second term, talk to

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Speaker 1: me about, I mean, I know why you wrote this piece,

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Speaker 1: because I think we're all starting to think about that

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Speaker 1: people are going to pretend they never supported Trump in

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Speaker 1: the first place.

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Speaker 2: Well, yeah, there'll be some of that. I mean, I

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Speaker 2: wrote it for New York Magazine. I first started talking

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Speaker 2: to them about it over a year ago. I felt

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Speaker 2: no one is looking the day after tomorrow, none of

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Speaker 2: the people who are sort of his collaborators. Now, I'm

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Speaker 2: not talking about MAGA voters, and I'm not talking about

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Speaker 2: crazy people. I'm not talking about Kash Patel or Pete

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Speaker 2: Hegseth or Pam Bondi or, you know, or Guilfoyle, those people.

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Speaker 2: What I'm talking about is the people who were accessories

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Speaker 2: possibly to crimes in his administration, who were once respectable people,

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Speaker 2: not MAGA, had real careers, were considered to be the

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Speaker 2: adults in the room. So people like Todd Blanche, who

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Speaker 2: was a prosecutor in the Southern District, or Besant, who

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Speaker 2: was a big deal on Wall Street and worked for

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Speaker 2: George Soros as a currency trader, or Jay Clayton, who's

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Speaker 2: now head of national security, who was a prominent a

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Speaker 2: highly regarded lawyer at, I think, Sullivan Cromwell. What are

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Speaker 2: they thinking is going to happen? And people are a

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Speaker 2: little bit shadier, like Howard Lutnick, whose wealth has doubled,

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Speaker 2: it's in the billions, has doubled since the second term began,

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Speaker 2: and whose sons, of course, are also getting rich, like

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Speaker 2: Trump's sons. And I'm thinking, Trump's going to come to

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Speaker 2: an end, the latest at the end of this term.

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Speaker 2: And do they think it's all just going to be forgotten?

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Speaker 2: Do they think that, oh, he'll give a preemptive pardon

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Speaker 2: to everybody, so no problem? No. Do they not understand

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Speaker 2: there's such thing as civil lawsuits? There's going to be

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Speaker 2: another Justice Department not run by Todd Blanch and staffed

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Speaker 2: by people like Jeanine Pirro that is going to conduct investigations.

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Speaker 2: There's going to be tremendous amount of memoirs and documents released,

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Speaker 2: including by people in high up jobs within executive branch

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Speaker 2: departments in the bureaucracy who want to save their own asses.

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Speaker 2: And there was a fascinating story in what was called Notice,

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Speaker 2: now called Washington Sun, a few weeks ago. There's something

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Speaker 2: like four of the seven top officials working under Besant

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Speaker 2: in Treasury Department have left. They were all Trump appointees.

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Speaker 2: They weren't civil service people. And why? They're worried they

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Speaker 2: might get disbarred if they do things about involving the

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Speaker 2: IRS and everything else that might be against the law.

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Speaker 2: So it's going to be, there's going to be a reckoning. And,

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Speaker 2: and I don't, it's incredible to me because it's not

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Speaker 2: like these people were idiots. Again, it's not like cash

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Speaker 2: for tell or Pete Hegg says, these people who actually

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Speaker 2: had real credentials were considered by their, uh, uh, you know,

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Speaker 2: colleagues to be smart, you know, added value to important,

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Speaker 2: you know, operations in, in private and public life. And

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Speaker 2: what they're just going to, it's all going to disappear

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Speaker 2: and everyone's going to forget. I don't think so. And

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Speaker 2: so that's what led to the peace because it led

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Speaker 2: me to look and see what people thought during the

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Speaker 2: World War II era when there were American executives like

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Speaker 2: top executive General Motors who helped make engines for Nazi

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Speaker 2: bombers before the war began and Wall Street firms and

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Speaker 2: banks including the Chase Bank that were helping appease the Nazis.

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Speaker 2: British Press lords. And it didn't end well for a

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Speaker 2: lot of them. And that was in an easier time. Now,

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Speaker 2: you can't bury your record so easily. And I thought

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Speaker 2: it was particularly hilarious when Trump, I don't even know

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Speaker 2: if he knows what AI is, but when he called

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Speaker 2: the AI executives to the White House at the end

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Speaker 2: of last month and said, we're not going to regulate

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Speaker 2: at all. It'll be voluntary regulation. He didn't realize that

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Speaker 2: AI is likely to to hack into records of his

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Speaker 2: White House when he was gone and his employees as

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Speaker 2: easily as they can in Venezuela or wherever. But he,

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Speaker 2: of course, isn't on this. He only cares about himself.

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Speaker 2: You'd think some of these other people would care about

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Speaker 2: what's going to happen to them.

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Speaker 1: I think it's a really good point. And one of

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Speaker 1: the things that you do really well in this piece

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Speaker 1: is you talk about anti-Semitism. And the way the right

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Speaker 1: has decided that they are avengers of anti-Semitism, but in fact,

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Speaker 1: they're actually just avengers of taxes. And I would love

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Speaker 1: you to talk about the case of Stephen Schwartzman.

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Speaker 2: Well, Stephen Schwartzman said there was one brief period last

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Speaker 2: time around, maybe after January 6th, where he criticized Trump.

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Speaker 2: For those who don't know, he's a billionaire who gives

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Speaker 2: a lot of money to Republican causes and a finance

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Speaker 2: guy in New York. And he said, this time, I'm

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Speaker 2: giving Republicans up and down the ticket as well as

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Speaker 2: supporting Trump because they're fighting anti-Semitism. Right. Here's a political

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Speaker 2: party where there have been Nazis that have dined at

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Speaker 2: Mar-a-Lago and at the New Jersey golf club of Trump.

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Speaker 2: The biggest anti-Semitic purveyors arguably in the United States are

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Speaker 2: people like Candace Owens and Tucker Carlson who have enormous audiences.

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Speaker 2: They're not Democrats. One could go on and on and on,

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Speaker 2: and the whole surge of these kind of neo-Nazi right-wingers

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Speaker 2: we saw begin in Charlottesville, of course, but now the Gropers,

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Speaker 2: they're becoming a big part of the Republican Party. These

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Speaker 2: young guys, very anti-Semitic, and I'm talking about real anti-Semitism

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Speaker 2: as opposed to being anti the Netanyahu government in Israel,

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Speaker 2: which should not be... conflated with anti-Semitism unless they actually

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Speaker 2: do join at some point in an individual person. But

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Speaker 2: these are people who actually think Jews should get out

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Speaker 2: of the country, out of America, and said so on

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Speaker 2: the record. And in some cases, they're January Sixers who

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Speaker 2: Trump pardoned. And I was struck by the fact that

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Speaker 2: back in the Obama years, when Obama's administration wanted to

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Speaker 2: change the tax law so that what's called carried interest

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Speaker 2: on Wall Street would be taxed, which is something that

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Speaker 2: would really affect Steve Schwartzman. He said at a public meeting,

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Speaker 2: this is like the Nazis coming in to Austria. And

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Speaker 2: I felt if that's what you think Nazism is, is

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Speaker 2: raising taxes, and then of course Trump is your candidate

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Speaker 2: because he's not going to raise taxes and everyone else

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Speaker 2: is a Nazi. Anyway, it's so stupid and it's embarrassing.

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Speaker 2: A lot of this is embarrassing. One other slightly different

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Speaker 2: example that's really haunted me involves Todd Blanche. When Todd

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Speaker 2: Blanche was confirmed as Attorney General in the Senate, he

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Speaker 2: brought with him to the hearing his wife, his daughter,

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Speaker 2: and his granddaughter. Now, he came to that hearing knowing

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Speaker 2: he was going to be questioned about what the hell

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Speaker 2: happened with the Epstein files, Why were they not released properly?

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Speaker 2: Why were they redacted? Why did he meet with Ghislaine Maxwell?

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Speaker 2: And how did she get moved to a country club prison?

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Speaker 2: I think his daughter and granddaughter are watching this. He

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Speaker 2: has no shame. Does he not know that there's going

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Speaker 2: to be a time, maybe after he's dead, we're going

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Speaker 2: to find out what really went on. And it's going

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Speaker 2: to be mortifying for him. But that shows how to

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Speaker 2: touch these people with reality and their cravenness to Trump.

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Speaker 1: Because we've covered such substantive issues, we must have a

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Speaker 1: tasty treat and talk. about Kimberly Guilfoyle, because that is

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Speaker 1: a story straight out of Veep. I'm sorry, but.

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Speaker 3: It's just- Oh, it is.

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Speaker 1: You got to get rid of the fiance. Sorry, go on.

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Speaker 2: Yeah, exactly. I mean, well, Kimberly Guilfoyle, I have to say,

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Speaker 2: Kristi Noem gives her a good run for the money,

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Speaker 2: because Kristi Noem's commercials that she made at, what, hundreds

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Speaker 2: of millions of dollars at taxpayer expense of like riding

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Speaker 2: on the open range on a horse. That's pretty amazing.

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Speaker 2: But Kimberly Guilfoyle, I love a former Trump's son, Paramore,

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Speaker 2: shift off to Greece where she presumably can't get in trouble,

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Speaker 2: be ambassador to Greece and have a nice paid vacation

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Speaker 2: and you know, drink white wine and lie on the

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Speaker 2: beach and whatever. And she's managed to try to turn

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Speaker 2: to a profit center and it incredible array of evidence,

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Speaker 2: incriminating evidence. I think the Wall Street Journal has run

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Speaker 2: three stories documenting her bragging to people about her freebies,

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Speaker 2: not to mention the incredibly embarrassing text messages begging a

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Speaker 2: sugar daddy to give $ 100, 000 to pay off her Amex bill.

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Speaker 2: And then her defense was, Their fake tech message, like,

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Speaker 2: I don't know, I guess AI wrote them for her

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Speaker 2: or something. It's pretty comic. And I'll say this about Guilfoyle,

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Speaker 2: at least. She's done less damage in that job, indeed,

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Speaker 2: than someone like Nomo actually ran a government department that

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Speaker 2: was with ICE and all of that. Maybe she's damaged

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Speaker 2: our relations with Greece, but that's about it. Where's Gavin Newsom,

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Speaker 2: by the way? He was actually married to her. I'd

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Speaker 2: like to hear his take on this.

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Speaker 1: It is, yes. It's straight out of Veep. You were,

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Speaker 1: unfortunately for you, a prophet. And with multiple shows where

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Speaker 1: you've predicted the future, let's hope that things go better.

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Speaker 2: Yeah, let's hope that things get better. Yeah, exactly. Yeah,

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Speaker 2: I mean, as Julie Dreyfuss said towards the end of

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Speaker 2: the show, when she was receiving the award, what we

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Speaker 2: started out as being a scathing satire ended up as

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Speaker 2: sobering documentaries. That's the trajectory of Veep.

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Speaker 1: Frank Rich, thank you, thank you.

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Speaker 2: Thank you, Molly. Wonderful to see you.

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Speaker 4: Molly, it's time for the headlines. And, you know, one

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Speaker 4: of the things you don't ever want to hear is

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Speaker 4: that your country's leaders are mulling martial law because they

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Speaker 4: may lose their majority and want to hold on to power.

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Speaker 4: It's really when you go, I think we're cooked.

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Speaker 1: So this is really important to just pull back for

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Speaker 1: a minute and remember that actually this is not the

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Speaker 1: first time that Donald Trump has discussed martial law. This

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Speaker 1: is kind of his go to. But thank you, Governor Polis,

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Speaker 1: for pardoning Tina Peters. Oh, no, I'm sorry. Not pardoning

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Speaker 1: for commuting. Yes. Tina Peters sentence. Thank you for that,

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Speaker 1: because that was a really good plan. Because Tina Peters has,

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Speaker 1: by the way, in case you're wondering, from a MAGA

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Speaker 1: election activist, Tina Peters has not learned her lesson, not

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Speaker 1: by any stretch of the imagination. This is a woman

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Speaker 1: who was convicted in 2024 of crimes tied to a

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Speaker 1: breach of election equipment. Now, Governor Polis, who is like,

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Speaker 1: who's like one of those Democrats, the way that Kristen

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Speaker 1: Sinema is a Democrat. I think that's fair.

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Speaker 4: Right. I mean, I mean, if I'm being real, I

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Speaker 4: don't see him doing any good for the public when

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Speaker 4: he's out of office.

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Speaker 2: Yeah.

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Speaker 1: He's the guy who, I mean, maybe his lover bodyguard

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Speaker 1: will destroy his self portraits too.

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Speaker 4: But if you, if you, if you wine tours paid

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Speaker 4: for wine tours paid for by lobbyists, maybe, you know?

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Speaker 2: Yeah.

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Speaker 1: I mean, like, Kristen Sinema may be the worst Democratic

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Speaker 1: senator ever, right? With few, I mean.

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Speaker 4: I mean, Joe Manchin would like a word, but he's

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Speaker 4: not having much of a word, yes.

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Speaker 1: Yeah, I'm sorry, but Joe Manchin is not worse than

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Speaker 1: Kristen Sinema, who is probably tied with John Baddresser Fetterman.

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Speaker 4: I mean, you know, he's sitting right there.

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Speaker 1: Do you guys want us to make a list, an

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Speaker 1: ongoing list of the worst members of the Senate and

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Speaker 1: the House? Should we be doing that? I feel like

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Speaker 1: that is the kind of public service that you and

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Speaker 1: I could do.

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Speaker 4: Right off the top of my head.

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Speaker 2: Right.

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Speaker 1: I know. Well, it's a real– it's a fool's errand.

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Speaker 4: Power rankings.

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Speaker 1: Yeah, power rankings. But back to the power rankings of

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Speaker 1: our terrifying news cycle. Basically, there is a real anxiety

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Speaker 1: here among people who can read newspapers that these guys

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Speaker 1: are going to do something really bad. And in my mind,

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Speaker 1: I just want the election to go off without a hitch.

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Speaker 1: And then if they try to fight the results, they're

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Speaker 1: going to lose in court. The thing that I'm more

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Speaker 1: worried about is them trying to interfere with the actual voting.

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Speaker 1: I think if we can get through the voting without

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Speaker 1: any problems, we're going to be in much better shape.

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Speaker 1: And by we, I mean American democracy writ large.

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Speaker 4: So there's a contingency plan shaping up for what they

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Speaker 4: do when they lose big, because also we should say

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Speaker 4: today there's a lot of headlines that it's looking even

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Speaker 4: worse than we thought for Republicans. It seems every week

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Speaker 4: The bloodbath escalations of polls being moved over to leading

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Speaker 4: Democrat just go up and up and up. So Senator

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Speaker 4: Bernie Marino, who would be on my list of most

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Speaker 4: pathetic senators of all time, he's saying cancel Thanksgiving recess

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Speaker 4: so we could ram down as much terrible legislation as

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Speaker 4: possible down the pike if we're going to lose the

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Speaker 4: majority in January.

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Speaker 1: So Senator Bernie Marino won Chair Brown Senate seat. So

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Speaker 1: if in the midterm Sherrod Brown wins, Bernie Marino and

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Speaker 1: Sherrod Brown would be the senators from Ohio, which would

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Speaker 1: be kind of spectacular. Look, Marino wants, this is, you

428
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Speaker 1: remember Marino was a car dealer. He is very tied

429
00:25:33,359 --> 00:25:36,350
Speaker 1: to a lot of different corporate interests, and he would

430
00:25:36,400 --> 00:25:39,730
Speaker 1: like to pass as much stuff. The goal here will

431
00:25:39,770 --> 00:25:45,810
Speaker 1: be Republicans will try to muddy the waters, soil the infrastructure,

432
00:25:45,910 --> 00:25:49,470
Speaker 1: do as much crazy stuff as they can, you know,

433
00:25:49,590 --> 00:25:51,890
Speaker 1: try to make it, you know, they'll do things like

434
00:25:53,190 --> 00:25:58,870
Speaker 1: make regulating AI illegal, stuff that's just totally fakakta in

435
00:25:59,109 --> 00:26:02,970
Speaker 1: the lame duck. And, you know, there's a lot of

436
00:26:03,080 --> 00:26:05,450
Speaker 1: anxiety that in the lame duck Trump is going to

437
00:26:05,520 --> 00:26:08,200
Speaker 1: try to fill if they lose the Senate, which this

438
00:26:08,220 --> 00:26:12,780
Speaker 1: is certainly possible. that they will try to fill those

439
00:26:12,859 --> 00:26:17,200
Speaker 1: two Supreme Court seats, Thomas and Alito, you know, because

440
00:26:17,320 --> 00:26:21,439
Speaker 1: Thomas and Alito are both really old. And those are

441
00:26:21,520 --> 00:26:25,959
Speaker 1: lifetime appointments. And I think Republicans saying very rightly that

442
00:26:25,980 --> 00:26:27,940
Speaker 1: if they lose the House and the Senate, they're probably

443
00:26:27,990 --> 00:26:32,290
Speaker 1: not winning it back anytime soon. And so that is,

444
00:26:32,310 --> 00:26:33,870
Speaker 1: I think, a real concern for them.

445
00:26:33,890 --> 00:26:34,390
Speaker 3: Yeah.

446
00:26:34,450 --> 00:26:40,450
Speaker 1: So watch if Democrats flip both houses and the midterm

447
00:26:40,490 --> 00:26:46,430
Speaker 1: elections don't tip us into some kind of terrifying post-America world,

448
00:26:47,030 --> 00:26:49,570
Speaker 1: we are going to have Republicans trying to do a

449
00:26:49,609 --> 00:26:54,570
Speaker 1: lot of chicanery during the lame duck.

450
00:26:55,530 --> 00:26:59,250
Speaker 4: Yeah. So speaking of losing it all, One of the

451
00:26:59,310 --> 00:27:02,370
Speaker 4: worst run campaigns I've seen, just one of the worst

452
00:27:02,450 --> 00:27:05,410
Speaker 4: candidates I've seen, is running against one of the best

453
00:27:05,470 --> 00:27:10,550
Speaker 4: campaigns I've seen, Mike Rogers versus Dr. Abdul-Aziz. And we're

454
00:27:10,590 --> 00:27:14,580
Speaker 4: now seeing that we've entered this bribing phase because that's

455
00:27:14,630 --> 00:27:16,710
Speaker 4: what type of gilded age we're in, that it's time

456
00:27:16,760 --> 00:27:20,160
Speaker 4: to bribe voters with things like Trump saying that'll give

457
00:27:20,240 --> 00:27:23,500
Speaker 4: everyone $ 5, 000. And now we have Mike Rogers' wife handing

458
00:27:23,520 --> 00:27:26,580
Speaker 4: out $ 10 gas cards after he begged for this war

459
00:27:26,660 --> 00:27:28,000
Speaker 4: in Iran for decades.

460
00:27:28,680 --> 00:27:29,040
Speaker 2: Yeah.

461
00:27:29,200 --> 00:27:34,040
Speaker 1: So Mike Rogers, super pro-war congressman, now running for Senate

462
00:27:34,100 --> 00:27:37,250
Speaker 1: in the great state of Michigan, which is getting, like

463
00:27:37,350 --> 00:27:40,470
Speaker 1: the rest of America, getting absolutely pummeled by the war

464
00:27:40,490 --> 00:27:46,110
Speaker 1: in Iran, which is making gas expensive. So Mike Rogers...

465
00:27:46,080 --> 00:27:49,210
Speaker 1: By the way, the... these guys live in Florida. So

466
00:27:49,590 --> 00:27:55,210
Speaker 1: watching her in Michigan, hanging out in like Detroit, right?

467
00:27:55,330 --> 00:27:58,670
Speaker 1: Not like gross point.

468
00:27:58,890 --> 00:28:01,119
Speaker 4: My joke was she probably went to Harris Teeter to

469
00:28:01,180 --> 00:28:02,200
Speaker 4: buy the gas cards.

470
00:28:02,980 --> 00:28:03,300
Speaker 2: Yeah.

471
00:28:03,460 --> 00:28:04,399
Speaker 1: I don't know what that is.

472
00:28:04,600 --> 00:28:07,380
Speaker 4: It's a, it's a gas, it's a grocery store located

473
00:28:07,400 --> 00:28:08,720
Speaker 4: in the South. That's the joke.

474
00:28:09,280 --> 00:28:09,480
Speaker 3: Yeah.

475
00:28:09,640 --> 00:28:14,040
Speaker 1: Okay. So she is, uh, she's handing out gas cards, looking, uh,

476
00:28:14,670 --> 00:28:19,150
Speaker 1: Pretty uncomfortable, all things considered. It's a video that you

477
00:28:19,170 --> 00:28:22,230
Speaker 1: should find if you are on the internet because it's

478
00:28:22,490 --> 00:28:29,000
Speaker 1: just absolutely worth watching. It is a little bit of hilariousness.

479
00:28:29,359 --> 00:28:30,230
Speaker 1: I recommend it.

480
00:28:31,359 --> 00:28:34,840
Speaker 4: Yeah, but it really tells you where they're at right now.

481
00:28:34,880 --> 00:28:38,120
Speaker 4: They're essentially trying to go person to person to buy

482
00:28:38,360 --> 00:28:42,000
Speaker 4: votes and pretend that he didn't wish for this all

483
00:28:42,040 --> 00:28:43,100
Speaker 4: the time, foolishly.

484
00:28:43,720 --> 00:28:43,960
Speaker 3: Yeah.

485
00:28:44,060 --> 00:28:48,280
Speaker 1: I also think it's just it's like they just don't

486
00:28:48,420 --> 00:28:54,380
Speaker 1: have many choices left because they're not, you know, they're

487
00:28:54,550 --> 00:28:59,050
Speaker 1: they can't like in a normal midterm, you could run

488
00:28:59,170 --> 00:29:01,930
Speaker 1: against the party in power, even if you're a Republican,

489
00:29:01,970 --> 00:29:03,810
Speaker 1: like you could say, well, I wouldn't have done this

490
00:29:03,890 --> 00:29:07,160
Speaker 1: or I wouldn't have done that. But we know that

491
00:29:07,180 --> 00:29:10,570
Speaker 1: if you do that, like Byron Donald taking Trump off

492
00:29:10,610 --> 00:29:14,250
Speaker 1: the website, people– Trump gets really mad and comes and

493
00:29:14,290 --> 00:29:17,990
Speaker 1: campaigns with you as punishment. So that makes it even–

494
00:29:18,090 --> 00:29:21,770
Speaker 1: I mean like Alaska is a great example of that.

495
00:29:21,790 --> 00:29:24,060
Speaker 4: Depending on how attractive you are as a man, he

496
00:29:24,080 --> 00:29:26,320
Speaker 4: either tells you you look like shit or you really

497
00:29:26,460 --> 00:29:28,740
Speaker 4: got a nice ass when he comes to campaign with you.

498
00:29:29,340 --> 00:29:35,060
Speaker 1: Yeah, disinhibited is the phrase, is the word that describes

499
00:29:35,100 --> 00:29:39,750
Speaker 1: Donald Trump talking about men's appearances in his 80th year

500
00:29:39,790 --> 00:29:39,980
Speaker 1: of law.

501
00:29:41,400 --> 00:29:44,140
Speaker 4: Okay, so Molly, as we know, I have a sick,

502
00:29:44,200 --> 00:29:48,100
Speaker 4: sick habit, which is watching these terrible debates. And one

503
00:29:48,120 --> 00:29:50,400
Speaker 4: of the things I've really noticed, it was funny to

504
00:29:50,420 --> 00:29:52,950
Speaker 4: see this headline today, is I was like, I feel

505
00:29:52,990 --> 00:29:55,950
Speaker 4: like this is the most mad at each other I've

506
00:29:55,970 --> 00:30:01,310
Speaker 4: seen candidates ever. That they just seem... Like, each one

507
00:30:01,350 --> 00:30:04,180
Speaker 4: of them has a hatred for one another that, like,

508
00:30:04,200 --> 00:30:06,320
Speaker 4: you would think ran seven generations.

509
00:30:07,220 --> 00:30:12,850
Speaker 1: Yeah, the– a great example is the Mike Collins.

510
00:30:13,530 --> 00:30:14,870
Speaker 4: Debate. John Ossoff.

511
00:30:15,310 --> 00:30:20,280
Speaker 1: John Ossoff debate in Georgia because like Chris Hayes was like,

512
00:30:20,560 --> 00:30:22,320
Speaker 1: it seems like you really don't like him.

513
00:30:25,440 --> 00:30:28,120
Speaker 4: And like, literally we see last night, Troy Jackson, not

514
00:30:28,140 --> 00:30:32,330
Speaker 4: known for being the most, you know, smiley guy. He

515
00:30:32,370 --> 00:30:36,930
Speaker 4: literally said, Oh, shut up. Like, and then Mike Lawler

516
00:30:36,990 --> 00:30:37,810
Speaker 4: and Kate Connelly.

517
00:30:38,780 --> 00:30:41,460
Speaker 1: Wait, I just want to say one thing about Troy Jackson.

518
00:30:41,560 --> 00:30:44,070
Speaker 1: If Troy Jackson ends up being a senator from Maine,

519
00:30:44,090 --> 00:30:46,510
Speaker 1: which I actually don't believe he'll win because I think

520
00:30:46,830 --> 00:30:50,490
Speaker 1: that Susan Collins is just unbeatable for whatever reason. I

521
00:30:50,530 --> 00:30:54,470
Speaker 1: think she's able to pretend to not be a Republican

522
00:30:54,530 --> 00:30:58,750
Speaker 1: enough for Maine voters. But if Troy Jackson wins, he

523
00:30:58,790 --> 00:31:01,010
Speaker 1: will be the most hilarious senator.

524
00:31:01,290 --> 00:31:03,350
Speaker 4: Oh, yeah, yeah, yeah, yeah. It'll be very funny.

525
00:31:03,730 --> 00:31:07,130
Speaker 1: Like him and Angus King, like that's a really good

526
00:31:07,170 --> 00:31:10,209
Speaker 1: match because the two of them will be like– the

527
00:31:10,290 --> 00:31:12,020
Speaker 1: hearings will be amazing.

528
00:31:13,200 --> 00:31:19,780
Speaker 4: Yeah, and really just a grouchy, grouchy dude. Hilarious. Particularly too,

529
00:31:19,900 --> 00:31:23,060
Speaker 4: like Kate Conley– I know you and I find Mike

530
00:31:23,160 --> 00:31:27,190
Speaker 4: Lawler to really be one of the more egregious congressmen

531
00:31:27,210 --> 00:31:28,510
Speaker 4: in just his bold-faced lies. Just a word.

532
00:31:29,330 --> 00:31:31,970
Speaker 1: On Mike Lawler, this is a guy who is in

533
00:31:31,990 --> 00:31:35,430
Speaker 1: the Hudson Valley, so it should be a blue seat.

534
00:31:35,550 --> 00:31:40,930
Speaker 1: It was Sean Patrick Maloney's seat. It's been a blue

535
00:31:40,950 --> 00:31:43,110
Speaker 1: seat for a long time. And what I think is

536
00:31:43,250 --> 00:31:47,770
Speaker 1: interesting about Mike Lawler is he sucks. And we all

537
00:31:47,810 --> 00:31:51,230
Speaker 1: hate him because– and hate is a strong word, but

538
00:31:51,490 --> 00:31:51,990
Speaker 1: it works here.

539
00:31:53,670 --> 00:31:57,450
Speaker 4: Not for me. It's kind of an underestimation how much

540
00:31:57,490 --> 00:31:58,130
Speaker 4: I dislike him.

541
00:31:58,440 --> 00:32:00,880
Speaker 1: Because he pretends to be a centrist. He goes on

542
00:32:01,020 --> 00:32:04,510
Speaker 1: MS now. But he's like a big Trump humper. He

543
00:32:04,670 --> 00:32:09,030
Speaker 1: loves Trump. And Trump actually came and didn't he come

544
00:32:09,070 --> 00:32:12,070
Speaker 1: and campaign with him? Or he said he threatened to

545
00:32:12,110 --> 00:32:13,210
Speaker 1: come and campaign with him.

546
00:32:14,180 --> 00:32:18,520
Speaker 4: Yeah. It really is just one of those things that he,

547
00:32:18,800 --> 00:32:24,870
Speaker 4: the way he lies about facts is a just Like

548
00:32:25,110 --> 00:32:28,230
Speaker 4: they all do it, but there's a smugness in his

549
00:32:28,830 --> 00:32:32,090
Speaker 4: ability to do it that just really rubs me so terribly.

550
00:32:32,710 --> 00:32:33,030
Speaker 3: Yeah.

551
00:32:33,450 --> 00:32:38,800
Speaker 1: And actually Trump loves him, right? Trump says he's fantastic.

552
00:32:38,960 --> 00:32:41,700
Speaker 1: So one of the things that Mike Lawler did in

553
00:32:41,820 --> 00:32:44,620
Speaker 1: one of the debates was he said, you are connected.

554
00:32:44,700 --> 00:32:48,660
Speaker 1: You took money from Reid Hoffman and Reid Hoffman went

555
00:32:48,680 --> 00:32:52,640
Speaker 1: to Epstein Island. Reid Hoffman is not like the central

556
00:32:52,770 --> 00:32:56,370
Speaker 1: person that people think of. when they think of Jeffrey Epstein,

557
00:32:56,890 --> 00:32:59,330
Speaker 1: it's Donald fucking Trump.

558
00:32:59,350 --> 00:33:02,030
Speaker 4: I think of the guy that Jeffrey Epstein said I

559
00:33:02,280 --> 00:33:04,740
Speaker 4: was his best friend for 10 years on tape. That's

560
00:33:04,760 --> 00:33:06,740
Speaker 4: who I think of when I think of Jeffrey Epstein.

561
00:33:06,760 --> 00:33:09,140
Speaker 1: And the guy who was like, we can't release the

562
00:33:09,200 --> 00:33:12,100
Speaker 1: Epstein files. And the administration that has spent all its

563
00:33:12,120 --> 00:33:14,960
Speaker 1: time trying to not release the Epstein files. That's who

564
00:33:15,020 --> 00:33:17,400
Speaker 1: I think of. So for Mike Lawler to bring that

565
00:33:17,480 --> 00:33:20,460
Speaker 1: up to Kate Connolly, who's like a veteran and a

566
00:33:20,640 --> 00:33:28,130
Speaker 1: woman and who is like, And she was pretty good

567
00:33:28,270 --> 00:33:30,890
Speaker 1: on that. I mean, I actually think she's kind of

568
00:33:30,930 --> 00:33:31,890
Speaker 1: a great candidate.

569
00:33:32,170 --> 00:33:35,500
Speaker 4: She did way better at those debates than she was

570
00:33:35,540 --> 00:33:36,640
Speaker 4: when I was seeing her early on.

571
00:33:38,740 --> 00:33:41,000
Speaker 1: Ali Velshi is the host of The 11th Hour with

572
00:33:41,140 --> 00:33:44,860
Speaker 1: Ali Velshi on MSNOW, as well as the upcoming show,

573
00:33:44,960 --> 00:33:49,640
Speaker 1: Sparking Outrage, Data Centers and AI Backlash. And Ali and

574
00:33:49,720 --> 00:33:55,600
Speaker 1: Stephanie have a new weekly show for MSNOW members, Velshi

575
00:33:55,680 --> 00:33:56,380
Speaker 1: and Rule.

576
00:33:56,940 --> 00:33:58,220
Speaker 3: Full disclosure.

577
00:33:58,260 --> 00:33:59,870
Speaker 1: Welcome, welcome, Ali Velshi.

578
00:33:59,880 --> 00:34:00,850
Speaker 3: Nice to be with you, my friend.

579
00:34:01,330 --> 00:34:04,990
Speaker 1: Nice to be with you. And we both have lasted

580
00:34:05,110 --> 00:34:10,160
Speaker 1: longer than... than Donald Trump's press secretary. I mean, that

581
00:34:10,200 --> 00:34:14,900
Speaker 1: was incredible when people, you know, by the time everyone

582
00:34:14,940 --> 00:34:18,339
Speaker 1: learned to pronounce Zachariah, she was out. Out.

583
00:34:18,780 --> 00:34:21,640
Speaker 3: I mean, look, I think on one hand, if you

584
00:34:21,660 --> 00:34:24,960
Speaker 3: just put aside the entire abnormality of this administration in

585
00:34:24,980 --> 00:34:26,939
Speaker 3: which anything goes and the world is always upside down,

586
00:34:27,580 --> 00:34:30,799
Speaker 3: I thought she was out from the first few words

587
00:34:30,950 --> 00:34:33,090
Speaker 3: she said. And not, I don't mean this in a

588
00:34:33,110 --> 00:34:36,160
Speaker 3: critical way. Everybody's not made for certain jobs. she seemed

589
00:34:36,440 --> 00:34:40,500
Speaker 3: out of her comfort zone and super uncomfortable with talking

590
00:34:40,540 --> 00:34:42,950
Speaker 3: to people, right? Some of these press records are good

591
00:34:42,969 --> 00:34:45,230
Speaker 3: at arguing. Some of them are good at banter. Some

592
00:34:45,250 --> 00:34:48,770
Speaker 3: of them are fast. She just doesn't look like what

593
00:34:48,830 --> 00:34:50,390
Speaker 3: she was meant to do. So I thought that was

594
00:34:50,450 --> 00:34:52,069
Speaker 3: rough to start with. And then the whole thing about

595
00:34:52,110 --> 00:34:54,759
Speaker 3: her law degree versus being an attorney versus being a

596
00:34:54,820 --> 00:34:58,440
Speaker 3: lawyer and passing your bar exam in China. At some point,

597
00:34:58,940 --> 00:35:02,420
Speaker 3: these are mistakes that I'm just puzzled are made by

598
00:35:02,460 --> 00:35:04,739
Speaker 3: people at the highest level of importance in this country.

599
00:35:04,760 --> 00:35:06,660
Speaker 3: And then the other thing, Molly, that I always think about,

600
00:35:07,160 --> 00:35:10,260
Speaker 3: there are 340 million people in this country, right? Canada

601
00:35:10,300 --> 00:35:12,480
Speaker 3: has 40 million people. And we could still find people

602
00:35:12,500 --> 00:35:15,930
Speaker 3: who are, generally speaking, qualified for jobs. How in 340

603
00:35:15,930 --> 00:35:18,509
Speaker 3: million people do they keep making blunders like that?

604
00:35:18,530 --> 00:35:22,370
Speaker 1: Well, it feels like Trump 1.0, right? Where Donald Trump

605
00:35:22,410 --> 00:35:25,410
Speaker 1: sees her on television, calls her up. We have a

606
00:35:25,530 --> 00:35:28,390
Speaker 1: lot of reporting that says Susie Wiles was not involved.

607
00:35:28,530 --> 00:35:31,109
Speaker 1: And so clearly people want everyone to know that this

608
00:35:31,170 --> 00:35:35,620
Speaker 1: was not their handiwork, which reminds me of Trump 1.0, right?

609
00:35:35,660 --> 00:35:38,460
Speaker 1: Where you could read a piece and know who was

610
00:35:38,520 --> 00:35:40,100
Speaker 1: doing the leaking, right?

611
00:35:40,200 --> 00:35:40,279
Speaker 3: Yes.

612
00:35:40,860 --> 00:35:42,900
Speaker 1: Like that seems to be going more and more. We

613
00:35:42,940 --> 00:35:46,319
Speaker 1: see different fractions acting out their fights in the Washington

614
00:35:46,350 --> 00:35:48,850
Speaker 1: Post and the New York Times. But I thought it

615
00:35:48,890 --> 00:35:52,049
Speaker 1: was really interesting to watch this because it was like

616
00:35:52,410 --> 00:35:55,130
Speaker 1: clearly he saw her on television and she even said

617
00:35:55,210 --> 00:35:57,640
Speaker 1: she had sort of gone on the shows he liked.

618
00:35:58,010 --> 00:36:00,380
Speaker 1: He called her up. She came in to the office

619
00:36:00,680 --> 00:36:02,400
Speaker 1: and the rest is history.

620
00:36:02,540 --> 00:36:03,279
Speaker 2: So you get a job.

621
00:36:03,940 --> 00:36:06,279
Speaker 3: And, you know, we we we've all been talking about

622
00:36:06,300 --> 00:36:09,000
Speaker 3: the fact that she's lasted, I think, 40 percent of

623
00:36:09,020 --> 00:36:11,410
Speaker 3: a Scaramucci or something of that nature. But I was

624
00:36:11,469 --> 00:36:13,759
Speaker 3: talking to Ali Vitale, who says hello, by the way,

625
00:36:13,780 --> 00:36:17,640
Speaker 3: and she was saying, one thing about Scaramucci, he got

626
00:36:17,660 --> 00:36:23,480
Speaker 3: in trouble because he was quoted saying things about other factions, right?

627
00:36:24,120 --> 00:36:27,190
Speaker 3: But nobody thought he was sort of not qualified for

628
00:36:27,250 --> 00:36:30,089
Speaker 3: the job. That's the difference. He fell out. He said

629
00:36:30,130 --> 00:36:32,669
Speaker 3: things and then Trump got rid of him. This just

630
00:36:32,810 --> 00:36:35,089
Speaker 3: didn't feel like any part of it. But in the

631
00:36:35,130 --> 00:36:39,959
Speaker 3: same way, I'm reminded that Peter Navarro, who's super influentialness

632
00:36:40,120 --> 00:36:44,580
Speaker 3: in this administration. Trump said stuff about China and then

633
00:36:44,640 --> 00:36:48,000
Speaker 3: told Jared Kushner to go find an economist who hates

634
00:36:48,080 --> 00:36:50,800
Speaker 3: China as much as he does. And Jared Kushner found

635
00:36:51,239 --> 00:36:55,549
Speaker 3: Peter Navarro on Amazon, literally found him on Amazon. And

636
00:36:55,590 --> 00:36:58,250
Speaker 3: then he became this senior advisor to Donald Trump. So,

637
00:36:58,250 --> 00:37:00,569
Speaker 3: you know what I'm saying? Like, this is how they roll.

638
00:37:00,989 --> 00:37:04,850
Speaker 1: Now, Scaramucci worked in finance, so he was he was

639
00:37:04,910 --> 00:37:07,370
Speaker 1: actually very good on television, but he did not have

640
00:37:08,210 --> 00:37:11,770
Speaker 1: He did not have a lot of qualifications for the job. Whereas,

641
00:37:12,190 --> 00:37:17,670
Speaker 1: you know, Zachariah did actually look like Natalie Harp, which.

642
00:37:17,710 --> 00:37:22,270
Speaker 3: In Trump world... 100%. 100%. Although I will say Scaramucci

643
00:37:22,290 --> 00:37:25,310
Speaker 3: presented himself well and was a good talker. Right. And

644
00:37:25,370 --> 00:37:27,650
Speaker 3: one of the problems here, maybe you have to look

645
00:37:27,690 --> 00:37:30,180
Speaker 3: a certain way for this job, but given the pressure

646
00:37:30,200 --> 00:37:33,360
Speaker 3: that the White House is under, the unpopularity, the Iran war,

647
00:37:33,719 --> 00:37:37,600
Speaker 3: the tariff wars, the banning of the media... you should

648
00:37:37,680 --> 00:37:39,719
Speaker 3: want and should be able to get, if you're the

649
00:37:39,739 --> 00:37:45,300
Speaker 3: White House, the most qualified public relations professional, crisis management

650
00:37:45,340 --> 00:37:49,009
Speaker 3: professional on earth. They exist. They work for Edelman or

651
00:37:49,030 --> 00:37:51,030
Speaker 3: they work for the big company, whatever, you know what

652
00:37:51,050 --> 00:37:52,920
Speaker 3: I mean? Get somebody from Ford or whatever the case is.

653
00:37:53,330 --> 00:37:55,530
Speaker 3: It doesn't need to be this way. That's the thing.

654
00:37:55,550 --> 00:37:57,970
Speaker 3: It just doesn't need to be this way. And Katie

655
00:37:57,989 --> 00:38:02,310
Speaker 3: Zachariah was not important enough in anybody's world. It's not

656
00:38:02,330 --> 00:38:03,950
Speaker 3: like she was carrying some influence that you needed to

657
00:38:03,969 --> 00:38:06,830
Speaker 3: keep some faction happy by having her there. But this

658
00:38:06,910 --> 00:38:09,089
Speaker 3: tells me that, you know, sometimes I worry a lot

659
00:38:09,110 --> 00:38:10,930
Speaker 3: about this administration and the damage it's going to do

660
00:38:10,989 --> 00:38:12,859
Speaker 3: and the damage it continues to do. And some days

661
00:38:12,890 --> 00:38:15,420
Speaker 3: I don't. Some days I think to myself, we will

662
00:38:15,460 --> 00:38:18,300
Speaker 3: be saved by their incompetence. And this is yet another example.

663
00:38:18,360 --> 00:38:20,040
Speaker 3: It's an embarrassing thing to happen to them.

664
00:38:20,320 --> 00:38:22,759
Speaker 1: Well, this is why it reminds me of Trump 1.0,

665
00:38:22,760 --> 00:38:26,390
Speaker 1: where they just would constantly make these cascades of mistaken

666
00:38:26,800 --> 00:38:30,040
Speaker 1: And now mistakes, and this actually leads me into what

667
00:38:30,060 --> 00:38:32,380
Speaker 1: we wanted to talk about next, which is the data centers.

668
00:38:32,440 --> 00:38:36,180
Speaker 1: You have a special you're doing on data centers, which

669
00:38:36,239 --> 00:38:38,580
Speaker 1: is really important because it's something you hear on the

670
00:38:38,620 --> 00:38:41,739
Speaker 1: trail all the time, but the mainstream media not super

671
00:38:42,080 --> 00:38:44,919
Speaker 1: read in on. Donald Trump, I just want to add,

672
00:38:45,180 --> 00:38:47,779
Speaker 1: when asked about data centers says like- Best thing in

673
00:38:47,800 --> 00:38:50,660
Speaker 1: the world. greatest, you know, and if you don't like them,

674
00:38:50,719 --> 00:38:51,780
Speaker 1: it's because you're stupid.

675
00:38:52,200 --> 00:38:53,620
Speaker 3: Yes. And if I were a mayor and I were

676
00:38:53,640 --> 00:38:55,609
Speaker 3: a governor, I'd want them all in there because, you know,

677
00:38:55,640 --> 00:38:58,130
Speaker 3: tariffs are his favorite word in the English language. Data

678
00:38:58,170 --> 00:39:00,170
Speaker 3: and center seem to be his second and third favorite

679
00:39:00,210 --> 00:39:02,630
Speaker 3: words in the English language. So we have this reporter,

680
00:39:02,650 --> 00:39:05,529
Speaker 3: Josh Einiger here, who decided that he wanted to make

681
00:39:05,550 --> 00:39:07,529
Speaker 3: this his beat. And I thought, I like Josh. He's

682
00:39:07,550 --> 00:39:08,739
Speaker 3: a nice guy and he comes on my show. So

683
00:39:08,760 --> 00:39:11,799
Speaker 3: I'm always happy to, you know, talk to him. But

684
00:39:12,260 --> 00:39:13,920
Speaker 3: I thought, I don't know, is that really a beat? Like,

685
00:39:13,960 --> 00:39:15,839
Speaker 3: is that a thing you want to do? And he

686
00:39:15,880 --> 00:39:18,080
Speaker 3: has gone all over this country and he's interviewed people

687
00:39:18,100 --> 00:39:20,719
Speaker 3: and he's interviewed companies. So we're doing a special on Sunday.

688
00:39:20,739 --> 00:39:22,490
Speaker 3: We're taking some of his best reporting. We went down

689
00:39:22,530 --> 00:39:25,430
Speaker 3: to Loudoun County, Virginia, which I didn't even really know this,

690
00:39:25,570 --> 00:39:28,110
Speaker 3: is the data center capital of the world. And a

691
00:39:28,150 --> 00:39:30,680
Speaker 3: year and a half, two years ago, Texas. and Virginia,

692
00:39:30,719 --> 00:39:32,779
Speaker 3: all these places were bragging about the fact that they

693
00:39:32,800 --> 00:39:35,279
Speaker 3: are attracting more data centers than anybody else. But here's

694
00:39:35,300 --> 00:39:38,140
Speaker 3: the thing. They run up your electricity. They use your water.

695
00:39:38,180 --> 00:39:40,149
Speaker 3: They need the water for cooling. They make a hell

696
00:39:40,170 --> 00:39:41,989
Speaker 3: of a lot of noise. If you want them off

697
00:39:42,030 --> 00:39:45,489
Speaker 3: the electrical grid, they have to generate their own electricity,

698
00:39:45,510 --> 00:39:49,810
Speaker 3: which generates pollution and smoke and more noise. And These

699
00:39:49,930 --> 00:39:55,460
Speaker 3: companies are getting tax breaks. I'm no socialist. Tax breaks

700
00:39:55,520 --> 00:39:59,280
Speaker 3: to the richest industry probably in the history of industry?

701
00:39:59,540 --> 00:40:03,080
Speaker 3: These people are richer than cigarettes, richer than pharma, richer

702
00:40:03,100 --> 00:40:06,440
Speaker 3: than oil, richer than railroad barons. Like they're literally the

703
00:40:06,500 --> 00:40:09,250
Speaker 3: richest industry on earth. How about you come into my

704
00:40:09,290 --> 00:40:11,359
Speaker 3: neighborhood and you build a data center and you build

705
00:40:11,390 --> 00:40:13,259
Speaker 3: a school and you build housing for everybody and you

706
00:40:13,280 --> 00:40:15,080
Speaker 3: build a new electrical grid and you do all those

707
00:40:15,100 --> 00:40:17,100
Speaker 3: things and maybe we'll let you come and do business

708
00:40:17,120 --> 00:40:19,259
Speaker 3: here because we know how badly you need your data center.

709
00:40:19,500 --> 00:40:22,880
Speaker 3: The concept of tax breaks, and by the way, nondisclosure agreements,

710
00:40:23,280 --> 00:40:26,320
Speaker 3: secret deals done with states, no hearings, no public hearings.

711
00:40:26,780 --> 00:40:29,770
Speaker 3: That's what people are pissed off about. It's like, maybe

712
00:40:29,850 --> 00:40:31,990
Speaker 3: I'm not going to be a Luddite. We need data centers.

713
00:40:32,010 --> 00:40:34,549
Speaker 3: They're server farms. But can we have some discussion about

714
00:40:34,590 --> 00:40:36,430
Speaker 3: where they go and why they're coming into my neighborhood?

715
00:40:36,650 --> 00:40:38,270
Speaker 3: And by the way, they sold everybody a bill of goods.

716
00:40:38,310 --> 00:40:40,430
Speaker 3: They said they're going to be job creators. They're warehouses.

717
00:40:40,690 --> 00:40:42,860
Speaker 3: They don't take a long time to build. And by definition,

718
00:40:43,160 --> 00:40:45,040
Speaker 3: they're servers. They'd need like two employees.

719
00:40:45,400 --> 00:40:47,620
Speaker 1: Well, and it gets to the Elon Musk of it,

720
00:40:47,660 --> 00:40:50,700
Speaker 1: which is today we learned that Elon Musk is basically

721
00:40:50,719 --> 00:40:53,200
Speaker 1: the largest Republican donor in the midterm cycle.

722
00:40:53,219 --> 00:40:53,299
Speaker 4: Yeah.

723
00:40:53,680 --> 00:40:58,410
Speaker 1: And he is the largest donor to Ken Paxton ever, basically.

724
00:40:58,489 --> 00:41:02,670
Speaker 1: And he is just bankrolling Ken Paxton. And Elon Musk

725
00:41:02,730 --> 00:41:06,950
Speaker 1: has made billions of dollars, almost a trillion dollars, in

726
00:41:07,260 --> 00:41:12,779
Speaker 1: tax write-offs, incentives, green incentives, different ways in which the

727
00:41:12,840 --> 00:41:18,520
Speaker 1: government has tried to incentivize technology and has instead made

728
00:41:18,600 --> 00:41:20,080
Speaker 1: Elon Musk the richest.

729
00:41:19,820 --> 00:41:20,219
Speaker 2: Man in the world.

730
00:41:20,239 --> 00:41:23,279
Speaker 3: And look, you know I'm a capitalist. I'm a business reporter.

731
00:41:24,050 --> 00:41:26,870
Speaker 3: I don't agree. In fact, there was often a Republican

732
00:41:27,410 --> 00:41:32,109
Speaker 3: view that the government shouldn't choose between potato chips and microchips, right?

733
00:41:32,130 --> 00:41:34,049
Speaker 3: In other words, the government should be agnostic as to

734
00:41:34,310 --> 00:41:36,509
Speaker 3: what people produce. I disagree with that. I think if

735
00:41:36,550 --> 00:41:39,800
Speaker 3: we need to be making important things like electric vehicles

736
00:41:39,940 --> 00:41:42,959
Speaker 3: or solar, we should actually give certain preferences to certain

737
00:41:43,000 --> 00:41:45,509
Speaker 3: industries to entice them to build. I happen to think

738
00:41:45,550 --> 00:41:48,109
Speaker 3: in the case of AI, China is our competitor. They

739
00:41:48,150 --> 00:41:50,049
Speaker 3: may not follow the same rules that we would all

740
00:41:50,070 --> 00:41:54,240
Speaker 3: want followed. So we actually probably should have some incentive

741
00:41:54,280 --> 00:41:57,279
Speaker 3: game going, but not this. These are the richest people

742
00:41:57,320 --> 00:41:58,500
Speaker 3: on Earth. By the way, in the midst of this

743
00:41:58,540 --> 00:42:01,120
Speaker 3: data center backlash that's been going on across the country,

744
00:42:01,160 --> 00:42:03,219
Speaker 3: there's very little that is as bipartisan as this, by

745
00:42:03,260 --> 00:42:06,190
Speaker 3: the way. Everybody hates these things. There is now this

746
00:42:06,489 --> 00:42:10,250
Speaker 3: discussion about whether AI will destroy humanity. Now, I don't

747
00:42:10,489 --> 00:42:13,130
Speaker 3: actually think that's true. I think that's a game that

748
00:42:13,150 --> 00:42:15,680
Speaker 3: the AI people are playing. to try and slow their

749
00:42:15,719 --> 00:42:17,460
Speaker 3: roll because they all want to IPO or at least

750
00:42:17,580 --> 00:42:20,020
Speaker 3: open AI and anthropic want to have, you know, they

751
00:42:20,040 --> 00:42:22,160
Speaker 3: want to go public on the stock market and this

752
00:42:22,200 --> 00:42:24,589
Speaker 3: stock market is going to run into some trouble pretty soon. Right.

753
00:42:24,640 --> 00:42:27,210
Speaker 3: It has been, it's been burning hot for a long

754
00:42:27,250 --> 00:42:29,790
Speaker 3: time and there's a lot of concern that it's going

755
00:42:29,810 --> 00:42:31,400
Speaker 3: to slow down. And I think this is all a,

756
00:42:31,989 --> 00:42:34,910
Speaker 3: a way to sort of dodge that bullet. I don't

757
00:42:34,950 --> 00:42:37,290
Speaker 3: think they believe it's going to, it's going to destroy humanity.

758
00:42:37,410 --> 00:42:39,840
Speaker 3: I will say when you read the stories about it,

759
00:42:39,840 --> 00:42:42,739
Speaker 3: you talk about these AI agents going rogue and the thing,

760
00:42:43,040 --> 00:42:47,969
Speaker 3: it's not true. It's software. If your computer hacks my computer,

761
00:42:48,530 --> 00:42:51,270
Speaker 3: I can call the police and the FBI and you

762
00:42:51,310 --> 00:42:54,250
Speaker 3: can be charged with a crime because your computer didn't

763
00:42:54,570 --> 00:42:57,550
Speaker 3: wake up and say, I'm going to hack Molly's stuff.

764
00:42:57,870 --> 00:43:01,359
Speaker 3: You programmed it. You created a sandbox and it broke

765
00:43:01,390 --> 00:43:04,259
Speaker 3: out of the same sandbox because you're bad programmers. I

766
00:43:04,280 --> 00:43:06,600
Speaker 3: talked to Roger McNamee the other day who said, maybe

767
00:43:06,640 --> 00:43:08,640
Speaker 3: it's not that we don't want AI, but maybe you

768
00:43:08,680 --> 00:43:10,920
Speaker 3: should shut it down, put it back in the lab,

769
00:43:11,360 --> 00:43:13,640
Speaker 3: get it ready for prime time, and then release it

770
00:43:13,860 --> 00:43:17,320
Speaker 3: rather than releasing iterations every two weeks that are still

771
00:43:17,860 --> 00:43:20,399
Speaker 3: woefully flawed. Like for all the damage social media did,

772
00:43:20,420 --> 00:43:22,239
Speaker 3: this is going to be way worse if we don't

773
00:43:22,280 --> 00:43:22,819
Speaker 3: get a handle on it.

774
00:43:23,060 --> 00:43:26,000
Speaker 1: But I would also add that these people really want

775
00:43:26,060 --> 00:43:31,930
Speaker 1: regulatory capture too. So they want, they're looking for some

776
00:43:32,150 --> 00:43:36,270
Speaker 1: kind of regulation in order to prevent other people from taking,

777
00:43:36,290 --> 00:43:38,529
Speaker 1: you know, they know that the government can help them.

778
00:43:38,550 --> 00:43:40,890
Speaker 3: Yes, they want the regulation they want. They want the

779
00:43:40,930 --> 00:43:45,830
Speaker 3: regulation they want. And fundamentally in society, that's not how

780
00:43:45,930 --> 00:43:49,050
Speaker 3: regulation is supposed to work. You know, when Mitt Romney

781
00:43:49,070 --> 00:43:50,969
Speaker 3: ran against Barack Obama and he said, I'm going to

782
00:43:51,030 --> 00:43:53,830
Speaker 3: cut regulation by 50 percent. And I said, I don't

783
00:43:53,890 --> 00:43:56,170
Speaker 3: know what the right amount to cut regulation is. I

784
00:43:56,190 --> 00:43:58,420
Speaker 3: don't want regulations that say my meat has to be

785
00:43:58,460 --> 00:44:01,279
Speaker 3: stored at a particular temperature or you can't really have

786
00:44:01,340 --> 00:44:04,600
Speaker 3: live cockroaches and rats and their feces when you're making

787
00:44:04,640 --> 00:44:06,759
Speaker 3: my sandwich. Like there's some the fridge has to be

788
00:44:06,780 --> 00:44:10,150
Speaker 3: a certain temperature in the. paint has to be a certain, uh,

789
00:44:10,350 --> 00:44:12,230
Speaker 3: you know, be lead free. Like I don't want any

790
00:44:12,250 --> 00:44:14,509
Speaker 3: of those regulations to go away, but there are a

791
00:44:14,510 --> 00:44:16,649
Speaker 3: whole lot of bullshit regulations that should probably go away.

792
00:44:17,130 --> 00:44:20,430
Speaker 3: And that's the, the, the attitude should be, let's have

793
00:44:20,489 --> 00:44:25,340
Speaker 3: the correct regulation that is determined by regulators in concert

794
00:44:25,400 --> 00:44:28,339
Speaker 3: with the public, right? Republicans hate Lena Kahn, but all

795
00:44:28,380 --> 00:44:30,040
Speaker 3: she said is I didn't make one single new law.

796
00:44:30,320 --> 00:44:31,799
Speaker 3: I'm just enforcing the laws that are.

797
00:44:31,780 --> 00:44:32,239
Speaker 4: On the books.

798
00:44:32,280 --> 00:44:35,970
Speaker 3: There are laws on the books that, that, that, capture

799
00:44:36,010 --> 00:44:39,180
Speaker 3: the idea of product liability, right? Like cigarettes. If you

800
00:44:39,300 --> 00:44:42,160
Speaker 3: did something, if your product harms other people, that's what

801
00:44:42,200 --> 00:44:45,060
Speaker 3: Meta got in trouble for in court. We can use

802
00:44:45,120 --> 00:44:47,520
Speaker 3: product liability laws, but that's not what they want. They

803
00:44:47,560 --> 00:44:50,440
Speaker 3: want the regulation they want. They want Donald Trump, he

804
00:44:50,480 --> 00:44:53,140
Speaker 3: created an AI czar. The AI czar is meant to

805
00:44:53,280 --> 00:44:55,879
Speaker 3: not really actually hem them in. It's meant to give

806
00:44:55,920 --> 00:44:57,910
Speaker 3: them the freedom to do what they want to do with,

807
00:44:58,030 --> 00:45:00,830
Speaker 3: as you said, the protections from other entrants to the market.

808
00:45:01,010 --> 00:45:03,589
Speaker 3: This is the worst kind of regulatory capture there is.

809
00:45:04,060 --> 00:45:05,620
Speaker 3: This is Russian oligarchy.

810
00:45:05,680 --> 00:45:07,100
Speaker 1: It's crony capitalism.

811
00:45:07,340 --> 00:45:08,400
Speaker 3: Yes, it's crony capitalism.

812
00:45:08,440 --> 00:45:08,799
Speaker 4: That's right.

813
00:45:09,719 --> 00:45:12,620
Speaker 1: But it is interesting to me as we talk about

814
00:45:13,460 --> 00:45:16,940
Speaker 1: AI to get back to the sort of data of

815
00:45:16,980 --> 00:45:20,879
Speaker 1: it all, right? Which is that Donald Trump is– we're

816
00:45:20,960 --> 00:45:23,480
Speaker 1: less than 20 days from the midterms. I think– are

817
00:45:23,520 --> 00:45:25,090
Speaker 1: we 19 days from the midterms?

818
00:45:25,090 --> 00:45:26,200
Speaker 3: 19 days, yeah.

819
00:45:26,200 --> 00:45:30,030
Speaker 1: 19 days from the midterms. And we are the map

820
00:45:30,230 --> 00:45:32,510
Speaker 1: is like, I don't know. Have you ever seen anything

821
00:45:32,530 --> 00:45:36,049
Speaker 1: like this where there's more and more money is pouring

822
00:45:36,200 --> 00:45:39,640
Speaker 1: into the Republican side and the map keeps opening up

823
00:45:39,960 --> 00:45:40,680
Speaker 1: for Democrats?

824
00:45:41,080 --> 00:45:42,380
Speaker 3: Yeah. So I, you know, I spent a lot of

825
00:45:42,400 --> 00:45:43,779
Speaker 3: time in Pennsylvania and the other day I was in

826
00:45:43,840 --> 00:45:47,100
Speaker 3: Ohio states where there's nothing, there's no ads that are

827
00:45:47,140 --> 00:45:49,680
Speaker 3: not political ads and they're probably kind of ugly in

828
00:45:49,700 --> 00:45:52,610
Speaker 3: all cases. Like there's no ads about, Hey, I'm Molly.

829
00:45:52,770 --> 00:45:54,529
Speaker 3: And if you elect me, I'm going to do these

830
00:45:54,590 --> 00:45:57,840
Speaker 3: good things. It's all about, everybody else and how terrible

831
00:45:57,860 --> 00:46:00,680
Speaker 3: they are. They will have some influence, but I will say,

832
00:46:01,160 --> 00:46:03,020
Speaker 3: you and I talked a lot during the primary season,

833
00:46:03,560 --> 00:46:07,600
Speaker 3: Democratic enthusiasm is very high. And elections like this have

834
00:46:07,640 --> 00:46:09,420
Speaker 3: to do with two things, preferences, which we see in

835
00:46:09,440 --> 00:46:12,840
Speaker 3: the polling, and turnout, ground game, right? Texas is a

836
00:46:12,860 --> 00:46:15,160
Speaker 3: great example. There may be a whole lot of Republican

837
00:46:15,200 --> 00:46:18,310
Speaker 3: and independent Texans who don't want to vote for James Tallarico, right?

838
00:46:18,410 --> 00:46:20,850
Speaker 3: And because they've really painted a picture of James Tallarico

839
00:46:20,930 --> 00:46:22,750
Speaker 3: as like, well, you know, all sorts of things that

840
00:46:22,770 --> 00:46:25,009
Speaker 3: he's not. But they maybe don't want to vote for

841
00:46:25,050 --> 00:46:25,850
Speaker 3: Ken Paxton either.

842
00:46:26,210 --> 00:46:26,330
Speaker 1: Right.

843
00:46:26,370 --> 00:46:27,870
Speaker 3: They just can't bring themselves to do it. There are

844
00:46:27,910 --> 00:46:32,440
Speaker 3: Republicans in Arizona who don't they're so proud of of

845
00:46:32,500 --> 00:46:34,880
Speaker 3: John McCain. You know, they don't want to be a

846
00:46:34,920 --> 00:46:38,060
Speaker 3: party of election deniers running for major offices. There are

847
00:46:38,120 --> 00:46:40,140
Speaker 3: Republicans in Michigan.

848
00:46:40,180 --> 00:46:40,580
Speaker 2: Same thing.

849
00:46:40,620 --> 00:46:42,920
Speaker 3: You can vote for Jocelyn Benson for governor or you

850
00:46:42,940 --> 00:46:45,609
Speaker 3: can vote for an actual election denier. And I think

851
00:46:45,700 --> 00:46:48,230
Speaker 3: that's part of it, that there are some Republicans who

852
00:46:48,270 --> 00:46:50,669
Speaker 3: just don't have the enthusiasm for their candidates. And there's

853
00:46:51,170 --> 00:46:53,649
Speaker 3: very few Republicans, about 30 percent of the country, though,

854
00:46:54,190 --> 00:46:57,350
Speaker 3: has enthusiasm for Donald Trump. Nobody else does. So I

855
00:46:57,450 --> 00:47:01,130
Speaker 3: think this is very interesting. I will say Cook Political Report,

856
00:47:01,150 --> 00:47:02,770
Speaker 3: which sort of, you know.

857
00:47:03,250 --> 00:47:04,550
Speaker 1: They're hedging their bets.

858
00:47:05,170 --> 00:47:07,070
Speaker 3: Yeah, they sort of said that it'll probably be between

859
00:47:07,110 --> 00:47:11,120
Speaker 3: sort of 10 and 25 net seats. Democrats only need

860
00:47:11,180 --> 00:47:14,210
Speaker 3: four in the House, but they're saying 10 to 25.

861
00:47:14,200 --> 00:47:16,640
Speaker 3: Maybe it's a good night. Give them 40. It's not

862
00:47:16,640 --> 00:47:19,380
Speaker 3: 100 or 150 like we've seen in waves in the

863
00:47:19,400 --> 00:47:22,469
Speaker 3: past because the country is not structured that way anymore.

864
00:47:22,489 --> 00:47:25,470
Speaker 3: You just can't do that. Gerrymandering has prevented that from happening.

865
00:47:25,730 --> 00:47:28,690
Speaker 3: There's kind of a ceiling of about 35 districts that

866
00:47:28,730 --> 00:47:33,370
Speaker 3: are actually swingy. So I would say if the Democrats

867
00:47:33,390 --> 00:47:35,610
Speaker 3: are having a very strong night, you'll see upward of 35.

868
00:47:35,610 --> 00:47:38,910
Speaker 3: If they're having a good night, you'll see between 10

869
00:47:38,910 --> 00:47:41,200
Speaker 3: and 35. That's probably what it looks like right now

870
00:47:41,260 --> 00:47:41,660
Speaker 3: in the House.

871
00:47:42,050 --> 00:47:44,940
Speaker 1: But it is interesting to me because if you look

872
00:47:45,160 --> 00:47:48,300
Speaker 1: like what this what we've seen is the calculus is

873
00:47:48,520 --> 00:47:52,270
Speaker 1: if your seat is anything less than an R plus 20.

874
00:47:52,270 --> 00:47:53,950
Speaker 3: It could be in danger. Yeah, you're in danger. It's wild.

875
00:47:53,989 --> 00:47:57,610
Speaker 3: I mean, New York 21, which is the far northeast

876
00:47:57,710 --> 00:48:00,810
Speaker 3: corner of this state that borders Canada. It's Stefanik, right?

877
00:48:00,850 --> 00:48:04,969
Speaker 3: In Stefanik's, at least Stefanik's old district. They're neck and neck.

878
00:48:05,090 --> 00:48:07,189
Speaker 3: The Democrat is neck and neck. It's rural New York.

879
00:48:07,270 --> 00:48:09,029
Speaker 3: I don't know how that's all going to go down.

880
00:48:09,070 --> 00:48:10,549
Speaker 3: He doesn't have a ground game, so it's not clear.

881
00:48:10,770 --> 00:48:13,000
Speaker 3: But the idea that it's even polling neck and neck in,

882
00:48:13,010 --> 00:48:15,110
Speaker 3: I mean, because, you know, outside of this part of

883
00:48:15,150 --> 00:48:17,089
Speaker 3: New York, which is the most populated part of New York,

884
00:48:17,450 --> 00:48:19,859
Speaker 3: it can be very rural and very, very conservative. There's

885
00:48:19,880 --> 00:48:22,170
Speaker 3: not a lot of blue people. elsewhere in the state.

886
00:48:22,469 --> 00:48:25,689
Speaker 3: Mike Lawler, interesting story, right? He's just outside of New York.

887
00:48:25,870 --> 00:48:28,930
Speaker 3: He wins in a district that Kamala Harris won in.

888
00:48:29,310 --> 00:48:31,440
Speaker 3: We'll have to see what happens there. And then remember,

889
00:48:31,500 --> 00:48:33,960
Speaker 3: if it's not a big majority for the Democrats, let's

890
00:48:33,980 --> 00:48:36,379
Speaker 3: say it's five seats, there are going to be five

891
00:48:36,400 --> 00:48:40,979
Speaker 3: DSA people elected. We know that for a fact, right? Five,

892
00:48:41,060 --> 00:48:45,009
Speaker 3: maybe six elected across this country. They've all said they're

893
00:48:45,050 --> 00:48:47,750
Speaker 3: not going to vote for Hakeem Jeffries as Speaker Now,

894
00:48:47,850 --> 00:48:49,610
Speaker 3: I don't think they're going to vote for a Republican

895
00:48:49,670 --> 00:48:52,750
Speaker 3: as speaker. And one of the things that people learn,

896
00:48:53,150 --> 00:48:54,770
Speaker 3: and I've talked to a lot of progressives who were

897
00:48:54,810 --> 00:48:57,049
Speaker 3: elected who said, you know, you can have these big

898
00:48:57,090 --> 00:49:00,290
Speaker 3: conversations before you get elected. Then you get into Congress

899
00:49:00,310 --> 00:49:01,630
Speaker 3: and you realize if you don't vote for the speaker,

900
00:49:01,650 --> 00:49:03,160
Speaker 3: you're not going to be on any committees. You're not

901
00:49:03,180 --> 00:49:05,219
Speaker 3: going to bring any bacon home. You're not going to

902
00:49:05,239 --> 00:49:07,960
Speaker 3: have any influence. So you have to make decisions around that.

903
00:49:08,300 --> 00:49:11,420
Speaker 3: So we'll see what happens. We'll see whether Hakeem Jeffries

904
00:49:12,500 --> 00:49:15,880
Speaker 3: survives as speaker. I think he probably does. I think

905
00:49:16,120 --> 00:49:18,620
Speaker 3: depending how the Senate goes, Chuck Schumer may have a

906
00:49:18,620 --> 00:49:20,420
Speaker 3: tougher run of it but we'll see.

907
00:49:20,880 --> 00:49:23,440
Speaker 1: Yeah, let's talk about the Senate for a minute, because, like,

908
00:49:23,500 --> 00:49:27,719
Speaker 1: when we talk about this candidate quality question with these Republican,

909
00:49:27,760 --> 00:49:31,459
Speaker 1: you know, like Mike Rogers, who's, like, angry and doesn't

910
00:49:31,480 --> 00:49:33,779
Speaker 1: live in, who may live in Florida a little bit.

911
00:49:33,800 --> 00:49:36,280
Speaker 3: Sues his patience into oblivion.

912
00:49:36,360 --> 00:49:39,030
Speaker 1: Right. That's Roger Marshall.

913
00:49:39,420 --> 00:49:40,040
Speaker 2: Oh, Roger Rogers.

914
00:49:40,100 --> 00:49:40,419
Speaker 4: I'm sorry.

915
00:49:40,440 --> 00:49:40,680
Speaker 1: Right.

916
00:49:40,860 --> 00:49:42,779
Speaker 3: But Mike Rogers in Michigan.

917
00:49:42,800 --> 00:49:43,010
Speaker 2: Right.

918
00:49:43,110 --> 00:49:47,690
Speaker 1: With the wife, with the gas cards yesterday in Detroit.

919
00:49:47,730 --> 00:49:49,870
Speaker 3: Giving out $ 10 gas cards to people who are showing up.

920
00:49:49,890 --> 00:49:51,520
Speaker 3: It's like we just do that now. I just give

921
00:49:51,540 --> 00:49:55,200
Speaker 3: people money. $ 5, 000 for President Trump and $ 10 for him.

922
00:49:55,200 --> 00:49:56,480
Speaker 1: $ 10 gas. I'd rather have the $ 5, 000.

923
00:49:56,360 --> 00:49:56,480
Speaker 3: But.

924
00:49:58,000 --> 00:50:02,340
Speaker 1: And they maybe live in Florida and they're so angry.

925
00:50:02,780 --> 00:50:06,000
Speaker 1: So there are some really genuinely bad candidates.

926
00:50:06,020 --> 00:50:06,819
Speaker 4: There are, right.

927
00:50:07,420 --> 00:50:13,129
Speaker 1: And then you have like Adam Hamilton in Kansas who

928
00:50:13,230 --> 00:50:14,270
Speaker 1: is the pastor.

929
00:50:13,890 --> 00:50:16,690
Speaker 3: Of a megachurch. Yeah, might be a really interesting story.

930
00:50:17,130 --> 00:50:25,109
Speaker 1: Right. And Josh Turek and Rob Sand in Iowa. You

931
00:50:25,150 --> 00:50:29,230
Speaker 1: have like a very interesting map. Now, let's talk about,

932
00:50:29,330 --> 00:50:30,850
Speaker 1: so I want you to talk about the map, and

933
00:50:30,870 --> 00:50:32,810
Speaker 1: then I also want you to talk about where you

934
00:50:32,830 --> 00:50:35,630
Speaker 1: think Chuck Schumer falls in that world. Because I think

935
00:50:36,010 --> 00:50:39,970
Speaker 1: that a lot of these more populist farming candidates are

936
00:50:40,160 --> 00:50:42,319
Speaker 1: also anti-Schumer for different reasons.

937
00:50:42,340 --> 00:50:44,360
Speaker 3: Yeah, so I will say, first of all, none of

938
00:50:44,400 --> 00:50:47,779
Speaker 3: the ratings agencies have decided that the Senate is a

939
00:50:48,280 --> 00:50:50,899
Speaker 3: done deal for Democrats. It continues to be an uphill

940
00:50:50,920 --> 00:50:52,780
Speaker 3: battle for a lot of reasons. Let's start with Texas,

941
00:50:53,060 --> 00:50:54,020
Speaker 3: worst candidate in the country.

942
00:50:54,060 --> 00:50:55,700
Speaker 1: But there are seven flippable, there are seven toss-up seats.

943
00:50:55,719 --> 00:50:58,239
Speaker 3: There are seven flippable, but there are reasons why it

944
00:50:58,260 --> 00:51:00,390
Speaker 3: could still not go the other way. Number one, Texas

945
00:51:00,430 --> 00:51:01,850
Speaker 3: is a big fear dream. If it's a big night

946
00:51:01,910 --> 00:51:04,610
Speaker 3: for Democrats, they could win not only the Senate, but

947
00:51:04,630 --> 00:51:07,050
Speaker 3: they could win the governor and the lieutenant governor's races, maybe.

948
00:51:07,270 --> 00:51:10,779
Speaker 3: Chitala Rico could win that race, but- Let's put that

949
00:51:10,820 --> 00:51:13,700
Speaker 3: aside for a second. Alaska, Mary Paltola could win that race,

950
00:51:13,719 --> 00:51:15,359
Speaker 3: but let's put it aside for a second. That's going

951
00:51:15,380 --> 00:51:17,339
Speaker 3: to be a good night for Democrats if they do that.

952
00:51:17,770 --> 00:51:20,150
Speaker 3: Maine is still up in the air, right? People are

953
00:51:20,170 --> 00:51:23,290
Speaker 3: mad at Susan Collins, but Troy Jackson is not for

954
00:51:23,330 --> 00:51:26,209
Speaker 3: better or for worse. you know, he's not the last guy.

955
00:51:26,489 --> 00:51:30,049
Speaker 3: He's not necessarily that guy who, so that's still an

956
00:51:30,110 --> 00:51:30,650
Speaker 3: open discussion.

957
00:51:30,790 --> 00:51:34,390
Speaker 1: And Susan Collins is Kaiser Soze. I mean, she, you know,

958
00:51:34,430 --> 00:51:37,780
Speaker 1: because of her place in appropriations, she's made it clear

959
00:51:37,800 --> 00:51:38,219
Speaker 1: that she delivers.

960
00:51:38,239 --> 00:51:41,440
Speaker 3: She brings stuff back to the state. She does. North Carolina,

961
00:51:41,560 --> 00:51:43,819
Speaker 3: I think it'd be a surprise if Roy Cooper doesn't win.

962
00:51:43,860 --> 00:51:46,560
Speaker 3: In fact, the Republicans have pulled out, basically. There's no

963
00:51:46,580 --> 00:51:48,640
Speaker 3: more money going to Michael Watley. So they'll probably win that.

964
00:51:49,020 --> 00:51:52,819
Speaker 3: Ossoff is performing very well. He raised a ton of money.

965
00:51:52,920 --> 00:51:56,440
Speaker 3: I mean, Tallarico won the most money, but Ossoff came

966
00:51:56,480 --> 00:51:59,440
Speaker 3: in second. Big, big, big, big money. He performs very

967
00:51:59,460 --> 00:52:02,430
Speaker 3: well in debates. He performs very well in Congress. Still

968
00:52:02,590 --> 00:52:05,310
Speaker 3: uphill battle for Democrats, but I think Ossoff will hang on.

969
00:52:05,830 --> 00:52:08,290
Speaker 3: Michigan is a tough one. It is just a tough one.

970
00:52:10,489 --> 00:52:13,870
Speaker 3: Mike Rogers lost the last time he won by 20,000 votes,

971
00:52:13,890 --> 00:52:18,520
Speaker 3: so very close election. Abdul Syed does well, but I

972
00:52:18,900 --> 00:52:21,520
Speaker 3: remind you about one thing in the primary. It was very,

973
00:52:21,580 --> 00:52:24,310
Speaker 3: very close. He won by fewer votes than the third

974
00:52:24,350 --> 00:52:29,310
Speaker 3: candidate got, right? And we always think with these sort

975
00:52:29,350 --> 00:52:31,710
Speaker 3: of more to the left candidates, he's not a democratic socialist.

976
00:52:31,790 --> 00:52:34,009
Speaker 3: He doesn't call himself a socialist either. But these more

977
00:52:34,030 --> 00:52:35,620
Speaker 3: to the left candidates, we think the young people are

978
00:52:35,640 --> 00:52:36,779
Speaker 3: going to go out to vote with them.

979
00:52:36,800 --> 00:52:37,660
Speaker 2: It didn't happen.

980
00:52:38,100 --> 00:52:40,860
Speaker 3: He won, but it was a squeaker. And if you recall,

981
00:52:40,880 --> 00:52:44,320
Speaker 3: it was a super ugly campaign. Credit to Haley Stevens.

982
00:52:44,880 --> 00:52:47,290
Speaker 3: They came together and I think have tried to heal

983
00:52:47,310 --> 00:52:50,529
Speaker 3: some wounds. Dana Nessel, the attorney general, was very, very

984
00:52:50,650 --> 00:52:53,910
Speaker 3: angry at him over the Israel stuff. They did some

985
00:52:53,950 --> 00:52:56,230
Speaker 3: work together. And I, you know, but I talk to

986
00:52:56,270 --> 00:53:00,040
Speaker 3: people who say they're not convinced the wounds have healed enough.

987
00:53:00,080 --> 00:53:02,259
Speaker 3: And when your margin is something like 20,000 votes.

988
00:53:02,739 --> 00:53:03,580
Speaker 2: That matters.

989
00:53:03,980 --> 00:53:07,339
Speaker 1: Yeah. And that and if Democrats lose that seat, then

990
00:53:07,400 --> 00:53:09,359
Speaker 1: it's very hard to see how they have.

991
00:53:09,380 --> 00:53:09,980
Speaker 3: Very hard.

992
00:53:10,580 --> 00:53:10,960
Speaker 4: Yes.

993
00:53:10,980 --> 00:53:12,239
Speaker 3: So it's got to be a good night. It's got

994
00:53:12,260 --> 00:53:14,040
Speaker 3: to be a very good night. The other problem that

995
00:53:14,060 --> 00:53:15,040
Speaker 3: Saeed's got. So he's got it.

996
00:53:15,040 --> 00:53:15,420
Speaker 2: He's got it.

997
00:53:15,950 --> 00:53:18,270
Speaker 3: Wounds to mend with the Jewish community, which he's been

998
00:53:18,290 --> 00:53:20,410
Speaker 3: trying to do. He has wounds to mend with the

999
00:53:20,550 --> 00:53:22,960
Speaker 3: with the black community, the African-American community. But he's you know,

1000
00:53:22,969 --> 00:53:26,980
Speaker 3: you got Barack Obama going in there and Harris and

1001
00:53:27,020 --> 00:53:30,299
Speaker 3: Kamala Harris. So that carries a lot of weight. So

1002
00:53:30,680 --> 00:53:33,300
Speaker 3: that's what I'm going to be watching probably more closely

1003
00:53:33,340 --> 00:53:36,510
Speaker 3: than anything else. That said, if it's a very good

1004
00:53:36,570 --> 00:53:39,510
Speaker 3: night for Democrats in the Senate and they take the Senate,

1005
00:53:39,930 --> 00:53:41,989
Speaker 3: it's a really interesting question. Is that good or bad

1006
00:53:42,030 --> 00:53:42,589
Speaker 3: for Chuck Schumer?

1007
00:53:43,110 --> 00:53:43,280
Speaker 2: Right.

1008
00:53:43,340 --> 00:53:45,830
Speaker 3: Because there are some people elected who say, I won't

1009
00:53:45,870 --> 00:53:47,870
Speaker 3: vote for Chuck Schumer. On the other hand, if Chuck

1010
00:53:47,890 --> 00:53:50,210
Speaker 3: Schumer gets to win the Senate back, and he did

1011
00:53:50,250 --> 00:53:52,850
Speaker 3: make some good picks. I mean, Janet Mills was a

1012
00:53:52,870 --> 00:53:55,090
Speaker 3: good pick. It didn't work for her. She didn't win

1013
00:53:55,110 --> 00:53:57,160
Speaker 3: the nomination. Roy Cooper is a good pick.

1014
00:53:57,560 --> 00:53:59,739
Speaker 1: Roy Cooper and Sherrod Brown is a really good pick.

1015
00:53:59,760 --> 00:54:00,879
Speaker 3: Sherrod Brown is a good pick.

1016
00:54:01,380 --> 00:54:05,420
Speaker 1: It strikes me that we're not talking about Ohio. It

1017
00:54:05,480 --> 00:54:08,989
Speaker 1: feels like the polling I've seen, I really could see

1018
00:54:09,219 --> 00:54:12,890
Speaker 1: like Sherrod Brown versus an appointed senator feels like.

1019
00:54:13,620 --> 00:54:16,799
Speaker 3: So I was in Ohio on Monday and Tuesday, and

1020
00:54:18,780 --> 00:54:21,560
Speaker 3: Trump did something. He had some campaign in which he

1021
00:54:21,600 --> 00:54:23,060
Speaker 3: was talking about John Husted, who was apparently one of

1022
00:54:23,080 --> 00:54:25,219
Speaker 3: these guys he doesn't really like all that much. And

1023
00:54:25,239 --> 00:54:26,560
Speaker 3: then he started talking about how he was looking at

1024
00:54:26,580 --> 00:54:28,799
Speaker 3: his body, and he was checking his body up, up,

1025
00:54:28,840 --> 00:54:31,520
Speaker 3: and down. And then he said his ass. He said,

1026
00:54:31,540 --> 00:54:33,359
Speaker 3: I looked at his ass. He's got a great ass.

1027
00:54:33,420 --> 00:54:35,759
Speaker 3: It's like, dude, what are you talking about? What are

1028
00:54:35,800 --> 00:54:37,840
Speaker 3: you talking about? Donald Trump was once sort of a

1029
00:54:37,920 --> 00:54:41,569
Speaker 3: funny guy. engaging. You've met him in person. He actually

1030
00:54:41,590 --> 00:54:44,600
Speaker 3: can be very, very engaging. He's just nuts now. Like

1031
00:54:44,739 --> 00:54:47,640
Speaker 3: what are you talking about John Husted's ass for? Because

1032
00:54:47,680 --> 00:54:49,440
Speaker 3: I spent two days in Ohio and I spoke to

1033
00:54:49,460 --> 00:54:51,900
Speaker 3: a lot of voters, Republican and Democratic. And you know

1034
00:54:51,920 --> 00:54:55,819
Speaker 3: what nobody asked me about? John Husted's ass. But they

1035
00:54:55,860 --> 00:54:58,529
Speaker 3: really asked about tariff. They all talked about diesel. They

1036
00:54:58,550 --> 00:55:00,770
Speaker 3: all asked that Canada is their biggest trading partner. They

1037
00:55:00,810 --> 00:55:03,870
Speaker 3: asked about those, you know, everybody in the damn country

1038
00:55:03,910 --> 00:55:04,870
Speaker 3: cares about affordability.

1039
00:55:05,050 --> 00:55:10,240
Speaker 1: But not about butts. Um, Ali. Thank you.

1040
00:55:10,840 --> 00:55:11,830
Speaker 3: My pleasure. Nice talking to you.

1041
00:55:12,280 --> 00:55:13,280
Speaker 1: Nice talking to you.

1042
00:55:13,320 --> 00:55:19,719
Speaker 3: Thank you. Jesse cannon.

1043
00:55:20,580 --> 00:55:23,600
Speaker 4: My junk fast. So, um, I know you love nothing

1044
00:55:23,660 --> 00:55:26,850
Speaker 4: more than scrolling the timeline and seeing an AI ad.

1045
00:55:26,910 --> 00:55:29,770
Speaker 4: You just, you sit there, you go, thank God technology

1046
00:55:29,810 --> 00:55:32,529
Speaker 4: has brought us here. Um, I think there's a thing

1047
00:55:32,570 --> 00:55:33,810
Speaker 4: I think about it. You're going to, I don't think

1048
00:55:33,850 --> 00:55:35,730
Speaker 4: you and I have talked about this. I love that. Um,

1049
00:55:36,340 --> 00:55:38,480
Speaker 4: So I've been doing a reporting for a pet project

1050
00:55:38,540 --> 00:55:41,549
Speaker 4: and I found out that these AI companies have tried

1051
00:55:41,610 --> 00:55:45,609
Speaker 4: to ask even meta, uh, how they can target more

1052
00:55:45,710 --> 00:55:48,989
Speaker 4: low IQ people with, cause that's who will use AI.

1053
00:55:49,330 --> 00:55:53,410
Speaker 4: And then when I see a headline like 137, 137

1054
00:55:53,410 --> 00:55:57,630
Speaker 4: million in AI generated ads flood the midterms, I think, wow,

1055
00:55:57,930 --> 00:56:00,370
Speaker 4: some of the dumbest people are trying to target idiots

1056
00:56:00,410 --> 00:56:01,969
Speaker 4: with the worst ads I've ever seen.

1057
00:56:02,350 --> 00:56:03,130
Speaker 3: Basically.

1058
00:56:03,950 --> 00:56:08,830
Speaker 1: There are these AI generated ads and they are really

1059
00:56:09,210 --> 00:56:13,049
Speaker 1: unbelievably bad. So you have the New York mayor and

1060
00:56:13,090 --> 00:56:16,880
Speaker 1: governor strolling through a park, gardening together in a sun

1061
00:56:16,910 --> 00:56:22,060
Speaker 1: drenched suburban yard, promoting a new drug, a political delusion pill.

1062
00:56:22,500 --> 00:56:26,299
Speaker 1: I mean, this is like, it's just very, I mean,

1063
00:56:26,320 --> 00:56:28,120
Speaker 1: I guess Bruce Blakeman.

1064
00:56:28,200 --> 00:56:28,660
Speaker 2: Yes.

1065
00:56:28,760 --> 00:56:31,640
Speaker 4: Bruce Blakeman. One of the worst candidates ever.

1066
00:56:31,700 --> 00:56:35,410
Speaker 1: One of the worst candidates ever. I mean, I understand

1067
00:56:35,430 --> 00:56:39,689
Speaker 1: that Kathy Hochul is not so popular and is not

1068
00:56:39,750 --> 00:56:41,700
Speaker 1: known to be a gifted politician, though I think she's

1069
00:56:41,739 --> 00:56:42,299
Speaker 1: gotten better.

1070
00:56:43,340 --> 00:56:46,560
Speaker 4: Listen, there has never been a turnaround for my approval

1071
00:56:46,600 --> 00:56:49,200
Speaker 4: of a politician in my lifetime like Kathy Hochul.

1072
00:56:49,960 --> 00:56:58,690
Speaker 1: Yeah. Bruce Blackman is running basically on the plank that

1073
00:56:59,210 --> 00:57:04,310
Speaker 1: Zorian is anti-Semitic and that Bruce is for the Jews. Again,

1074
00:57:04,930 --> 00:57:07,229
Speaker 1: and it's worth listening to Frank Rich on this because

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00:57:07,270 --> 00:57:12,000
Speaker 1: it's really true. This is a party that is the

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00:57:12,060 --> 00:57:15,300
Speaker 1: party of both Tucker Carlson and Candace Owens, two of

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00:57:15,380 --> 00:57:16,959
Speaker 1: the most famous anti-Semites.

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00:57:17,900 --> 00:57:20,260
Speaker 4: Among hundreds of others.

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00:57:20,740 --> 00:57:21,540
Speaker 2: Right, exactly.

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00:57:24,020 --> 00:57:26,360
Speaker 1: But yes, all the stupidest people in the world are

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Speaker 1: using AI ads to try and win hearts and minds.

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00:57:30,470 --> 00:57:36,140
Speaker 1: And And FYI, I think this is really important. It's like,

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00:57:36,340 --> 00:57:41,210
Speaker 1: the government can outlaw AI ads tomorrow. They just have

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00:57:41,250 --> 00:57:42,030
Speaker 1: chosen not to.

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00:57:42,030 --> 00:57:45,970
Speaker 4: I actually was speaking with one of the people. So

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00:57:46,390 --> 00:57:50,480
Speaker 4: there's the, if people want AI ads, gone. There will

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00:57:50,520 --> 00:57:52,200
Speaker 4: be a law on the lame duck that will probably

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00:57:52,240 --> 00:57:54,390
Speaker 4: come up that is likely to go there. If you

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00:57:54,430 --> 00:57:58,110
Speaker 4: want to write your congressperson as somebody who's married to

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00:57:58,170 --> 00:58:01,790
Speaker 4: someone who works in constituent services, they actually do feel

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00:58:01,830 --> 00:58:03,440
Speaker 4: the pressure when people bring these things up.

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Speaker 1: Mm hmm. That's it for this episode of Fast Politics.

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Speaker 1: Tune in every Monday, Wednesday, Thursday, and Saturday to hear

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00:58:14,980 --> 00:58:19,360
Speaker 1: the best minds in politics make sense of all this chaos.

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00:58:19,820 --> 00:58:22,540
Speaker 1: If you enjoy this podcast, please send it to a

1096
00:58:22,620 --> 00:58:26,620
Speaker 1: friend and keep the conversation going. Thanks for listening.